1. Field of the Invention
The invention is related to image processing. More specifically, the invention relates to the enhancement of the image quality by reducing artifacts due to heavy quantization for compression.
2. Description of Related Art
One crucial aspect of imaging is the process of image compression. In digital systems such as digital cameras image compression is utilized to store and transmit a large amount of image information in fewest number of bits possible while still maintaining an image quality that is acceptable. One example of a popular image compression scheme is JPEG (Joint Photographic Experts Group) which is based upon the Discrete Cosine Transform (DCT). Recently, new image compression schemes have been developed utilizing the Discrete Wavelet Transform (DWT). Unlike the DCT which is periodic in nature, the DWT is better suited and often more efficient in representing sharp discontinuities in data such as that present in image edge features. The DWT decomposes an input image into a number of xe2x80x9csub-bandsxe2x80x9d in multiple levels which are shown in FIG. 1. FIG. 1 shows the results of iteratively applying a 2-dimensional (2-D) DWT to an image.
The first iteration of the 2-D DWT decomposes an image I into four sub-bands S0, S1, S2 and S3. The sub-band S0 is also referred to as the xe2x80x9cLLxe2x80x9d sub-band, based on the double low-pass filtering-used to generate it. S0 (LL) is essentially a scaled approximation of the original image I, and contains the most salient spatial information to the original image. The sub-bands S1, S2 and S3 contain edge information and when the input image is noisy, also a considerable amount of that noise. The sub-bands S1, S2, and S3 are also referred to as HL, LH and HH sub-bands, respectively, due to the various low-pass and high-pass filtering used to generate them. The level 1 sub-bands S0, S1, S2, and S3 result from applying the 2-D DWT once to the image I. Since LL sub-band is the scaled version of the original image, we can apply the same decomposition in it. If the 2-D DWT is applied again, to the sub-band S0, a level 2 2-D DWT is being performed. This would result in the generation of four new sub-bands S10, S11, S12, and S13 after decomposition to S0. After such decomposition, a mechanism known as quantization, which is the mapping of one range of values to a smaller range, is employed to yield compression. One such quantization technique, known as uniform scalar quantization, divides the original data points by a threshold number T (or quantized parameter) greater than or equal to 1, in order to achieve the mapping.
Since the sub-bands S2, S3 and S1 are perceptually (from a visual standpoint) less significant than the S0 sub-band, these sub-bands may be more xe2x80x9croughlyxe2x80x9d quantized (i.e., assigned a higher quantization threshold T) so that the values therein are compressed greater. Such scalar quantization may use one quantization threshold Ti throughout one sub-band Si and then different a Tj in another sub-band Sj. Accordingly, FIG. 1 shows a threshold T0 applicable to sub-band S3, a threshold T1 applicable to both sub-bands S1 and S2, T3 applicable to S13, T2 applicable S11 and S12 and T4 applicable to sub-band S10. These quantization thresholds maybe selected depending upon the desired level of compression by introducing quantization loss. The higher the quantization threshold, the more compression and hence more possible loss when the compressed image is reconstructed.
Up to a certain level of quantization, the loss incurred may not be visible by the human eyes. Such compression is referred to as visually lossless compression. For the application which warrants more compression beyond a visually lossless scheme, further compression can be achieved by using a higher quantization threshold than that used for visually lossless compression. As a result, however, visible artifacts will occur in the reconstructed image. We can enhance the compression performance, by reducing these visible artifacts.
When DWT coefficients (from applying a DWT upon an image) are then heavily quantized, xe2x80x9cRinging Artifactsxe2x80x9d are often produced in the decompressed (reconstructed) image. Due to the well-known Gibbs phenomenon, the Ringing Effect (which leads to Ringing Artifacts) places rings around a homogenous area of an image such as a clear sky or background. To reduce this effect, several digital filtering techniques have been devised, but these, when applied, have the additional effect of introducing smoothness or blurring. By reducing the sharpness of an image, such filtering, used to remove the Ringing Effect, also degrades image quality. It would be desirable to remove ringing artifacts while preserving the sharp edges which are the most perceived features by the human visual system.
What is disclosed is a method that includes defining a morphological filtering operator designed to remove ringing artifacts in a decompressed image, and then applying that operator upon pixels of the decompressed image that belong to non-texture image regions.